1. Data Products and Product-Mode Organizations
The concept of “data as a product” has gained traction, thinking about data as a critical business asset that needs to be managed, marketed, and optimized for value. Data products refer to any tool, platform, or feature that is built around datasets to provide insights or generate value for users.
What does this mean for data literacy? To effectively implement a data-as-a-product strategy, it’s important that all employees understand the concept and their role in making it successful. This requires clear and targeted training that not only explains what data-as-a-product means but also outlines how each employee contributes to its execution.
Additionally, for a data-as-a-product strategy to truly succeed, business teams must be involved from the start. They have insights into which data products will create real value for the company, whether it’s improving internal processes, enhancing customer experiences, or driving innovation. Training business teams to identify opportunities for data products—such as machine learning opportunities—empowers them to collaborate more effectively with data teams. This makes sure that the data products developed are not only technically sound but also directly aligned with business needs, ultimately making them more practical and impactful for the organization.
2. AI Governance and EU AI Act
With the AI Act going into force last summer, this is, of course, an increasingly important part of the training programs being developed. Besides adhering to compliance standards, organizations are also seeing the potential risks and looking for ways to communicate this effectively to their employees on a large scale. The question is how to do that without making it a mandatory click-through.
At Data Booster, our vision is to create training programs that integrate both AI governance and AI literacy. This means educating employees on the regulations and ethical standards they must follow (what they must do) while also equipping them with the skills and knowledge to harness AI’s potential in innovative ways (what they can do). By combining governance and literacy, we ensure that employees not only understand their legal obligations but also feel empowered to use AI responsibly to drive value for the organization. This holistic approach fosters a more engaged, knowledgeable workforce, capable of navigating both the opportunities and challenges that AI presents in the modern workplace.

3. Decision-Driven Analytics
The new frontier in data literacy is decision-driven analytics, where the goal is to tie analytics directly to business decisions. Decision-driven analytics is about using data to guide key decisions, rather than just collecting information or filling reports with endless charts. It focuses on identifying the most relevant data to generate insights that lead to meaningful actions. This approach encourages moving away from the mindset that all data is equally important and from being swayed by the latest tools. Instead, the emphasis is on clear, purposeful use of data to directly support decision-making and drive better outcomes. We recommend Stefano Puntoni’s book on decision-driven analytics where he explains the four pillars of decision-driven analytics.
This approach impacts data literacy programs because it aligns more effectively with individuals who don’t have a strong background in data or analytics. For many employees, especially those in non-technical roles, trying to extract insights from large piles of data without the necessary skills can be overwhelming. Instead of expecting them to become data experts, a better strategy is to teach them how to approach data with clear, structured business questions.
In many cases, these employees are already used to making decisions based on intuition or gut feeling. By framing data literacy training around answering these same types of business questions, but with data, the process becomes more accessible and relevant to their everyday work. This way, they learn to apply data in a way that feels familiar, structured, and understandable, ultimately helping them feel more confident in using data to inform decisions.
4. Data Creators
When rolling out data literacy initiatives the focus point is mostly on business professionals and specific skill training for data experts. However, there are large groups of people that impact how organizations can use their data enormously. People working daily with operating systems are influencing the data quality as they are at the beginning of the data pipeline. In every organization, this group is different they could be customer service, warehouse, package delivery, or people working in the stores.
We spoke to multiple organizations developing plans for programs that target these groups as well. The key here is to tailor it to different roles and to keep it short. Another thing to keep in mind is that these people do not work on laptops or devices all day. We have seen creative plans around this, like outreach via SMS instead of email, tablet-friendly training, and short videos you can watch from your phone. We expect this trend to continue in the upcoming years and looking forward to more creative ideas.
As we move forward, the companies that successfully integrate these trends into their data literacy strategies will be the ones best positioned to innovate, drive business value, and stay ahead in the competitive global market.